Details
Date: Aug 13, 2025
Location: Baltimore, MD - USA
Abstract
Predicting species’ responses to environmental change is a critical challenge in ecology. Traditional species distribution models (SDMs) often rely on correlative relationships, limiting their ability to forecast distributions under novel conditions. Dynamic range models (DRMs) offer a more mechanistic approach by explicitly incorporating demographic processes (e.g., recruitment, survival, and movement) that drive range dynamics. However, the complexity of DRMs has hindered their widespread adoption. We introduce drmr, an open-source R package that significantly lowers the barrier to entry for DRM applications. drmr provides a user-friendly framework for building, fitting, and projecting age-structured DRMs, leveraging the power of Stan for efficient Bayesian inference. Users can readily relate environmental drivers to ecological processes such as recruitment and mortality. There is also an option to allow for movement and limit the age groups that can move. This flexibility allows researchers to tailor models to specific ecological systems and test competing hypotheses about the factors driving change in species distributions. The package facilitates the integration of spatially and temporally explicit environmental data and provides tools for model evaluation, visualization, and projection under future scenarios. Through simulation studies, we demonstrate that drmr-based DRMs outperform traditional SDMs in forecasting accuracy under environmental change. drmr provides a powerful and accessible tool for ecologists to develop and apply mechanistic DRMs. An application using Summer Flounder data corroborates the findings of the simulation study. Specifically, the DRM’s root mean square error of prediction was 43% smaller than that of the comparable SDM. By explicitly modeling demographic processes and their environmental drivers, drmr enables more robust predictions of species distributions, contributing to improved conservation planning and management in the face of global change.